A Novel Privacy-Preserving Approach Using Optimized Deep Learning for Secure Data Mining

Rahul Reddy Bandhela; RamMohan Reddy Kundavaram; Abhishake Reddy Onteddu

doi:10.48084/etasr.15710

Authors

Rahul Reddy Bandhela CVS Pharmacy Inc., Sudbury, MA, USA https://orcid.org/0009-0003-2390-0642
RamMohan Reddy Kundavaram Dynamic Technology Inc., Naperville, IL, USA https://orcid.org/0009-0007-4376-1503
Abhishake Reddy Onteddu DPR Solutions Inc., Aurora, IL, USA https://orcid.org/0009-0003-3527-7077

Volume: 16 | Issue: 1 | Pages: 32585-32592 | February 2026 | https://doi.org/10.48084/etasr.15710

Received: 22 October 2025 | Revised: 26 November 2025, 26 December 2025, 3 January 2026, and 6 January 2026 | Accepted: 9 January 2026 | Online: 9 February 2026

Corresponding author: Rahul Reddy Bandhela

Abstract

Preservation of privacy involves the use of methods to protect sensitive data. Data mining is the derivation of various patterns and insights from big data using statistical and machine learning tools. A privacy-preserving data mining protocol follows a methodological system to ensure the safety of data encryption, improving key generation. The proposed system architecture offers a strong cloud-based platform for data encryption and retrieval. Data preservation is performed using Brakerski/Fan-Vercauteren (BFV), where data is encrypted with the help of a secret key and transformed with the help of a random matrix to increase security. The secret key is constructed using the Double Exponential Smoothing Secretary Bird Optimization Algorithm (DES-SBOA), combining the double exponential smoothing with the Secretary Bird Optimization Algorithm (SBOA). The encrypted data is stored safely in the cloud, ensuring that it will not be accessed by the wrong users, but can still be used to produce MLP outputs with an accuracy of 57.5%, a privacy of 39.8%, a utility of 98.5%, a fitness of 62.9%, and an execution time of 341.5s.

Keywords:

BFV homomorphic encryption, Double Exponential Smoothing Secretary Bird Optimization Algorithm (DES-SBOA), MLP, privacy preservation

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References

M. Kumar et al., "A smart privacy preserving framework for industrial IoT using hybrid meta-heuristic algorithm," Scientific Reports, vol. 13, no. 1, Apr. 2023, Art. no. 5372. DOI: https://doi.org/10.1038/s41598-023-32098-2

Z. Tang, M. Ye, Y. Liu, and S. Wei, "Privacy-Preserving Multimedia Mobile Cloud Computing Using Protective Perturbation." arXiv, Sept. 03, 2024.

R. Mendes and J. P. Vilela, "Privacy-Preserving Data Mining: Methods, Metrics, and Applications," IEEE Access, vol. 5, pp. 10562–10582, 2017. DOI: https://doi.org/10.1109/ACCESS.2017.2706947

Y. S. Hindistan and E. F. Yetkin, "A Hybrid Approach With GAN and DP for Privacy Preservation of IIoT Data," IEEE Access, vol. 11, pp. 5837–5849, 2023. DOI: https://doi.org/10.1109/ACCESS.2023.3235969

Y. V. R. S. Viswanadham, and K. Jayavel, "A Framework for Data Privacy Preserving in Supply Chain Management Using Hybrid Meta-Heuristic Algorithm with Ethereum Blockchain Technology," Electronics, vol. 12, no. 6, Mar. 2023. DOI: https://doi.org/10.3390/electronics12061404

L. Xu, X. Cheng, W. Tian, H. Wang, and Y. Zhang, "Cloud-Assisted Privacy-Preserving Spectral Clustering Algorithm Within a Multi-User Setting," IEEE Access, vol. 12, pp. 75965–75982, 2024. DOI: https://doi.org/10.1109/ACCESS.2024.3404265

Z. Tang, M. Ye, Y. Liu, and S. Wei, "Privacy-Preserving Multimedia Mobile Cloud Computing Using Protective Perturbation." arXiv, 2024. DOI: https://doi.org/10.1145/3712678.3721879

A. Ali et al., "HealthLock: Blockchain-Based Privacy Preservation Using Homomorphic Encryption in Internet of Things Healthcare Applications," Sensors, vol. 23, no. 15, July 2023. DOI: https://doi.org/10.3390/s23156762

S. T. Revathi, A. Gayathri, J. Kalaivani, M. S. Christo, D. Pelusi, and M. Azees, "Cloud-Assisted Privacy-Preserving Method for Healthcare Using Adaptive Fractional Brain Storm Integrated Whale Optimization Algorithm," Security and Communication Networks, vol. 2021, no. 1, 2021, Art. no. 6210054. DOI: https://doi.org/10.1155/2021/6210054

A. Majeed and S. Lee, "Anonymization Techniques for Privacy Preserving Data Publishing: A Comprehensive Survey," IEEE Access, vol. 9, pp. 8512–8545, 2021. DOI: https://doi.org/10.1109/ACCESS.2020.3045700

E. Hesamifard, H. Takabi, M. Ghasemi, and R. N. Wright, "Privacy-preserving Machine Learning as a Service," Proceedings on Privacy Enhancing Technologies, vol. 2018, no. 3, pp. 123–142, June 2018. DOI: https://doi.org/10.1515/popets-2018-0024

R. Agrawal and R. Srikant, "Privacy-preserving data mining," in Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, Dallas, TX, USA, May 2000, pp. 439–450. DOI: https://doi.org/10.1145/342009.335438

T. Hunt, C. Song, R. Shokri, V. Shmatikov, and E. Witchel, "Chiron: Privacy-preserving Machine Learning as a Service." arXiv, Mar. 15, 2018.

M. A. Sahi et al., "Privacy Preservation in e-Healthcare Environments: State of the Art and Future Directions," IEEE Access, vol. 6, pp. 464–478, 2018. DOI: https://doi.org/10.1109/ACCESS.2017.2767561

C. Clifton, M. Kantarcioglu, J. Vaidya, X. Lin, and M. Y. Zhu, "Tools for privacy preserving distributed data mining," ACM SIGKDD Explorations Newsletter, vol. 4, no. 2, pp. 28–34, Sept. 2002. DOI: https://doi.org/10.1145/772862.772867

S. Alabdulwahab, Y.-T. Kim, Y. Son, S. Alabdulwahab, Y.-T. Kim, and Y. Son, "Privacy-Preserving Synthetic Data Generation Method for IoT-Sensor Network IDS Using CTGAN," Sensors, vol. 24, no. 22, Nov. 2024. DOI: https://doi.org/10.3390/s24227389

N. Narula, W. Vasquez, and M. Virza, "zkLedger: Privacy-Preserving Auditing for Distributed Ledgers," presented at the 15th USENIX Symposium on Networked Systems Design and Implementation (NSDI 18), 2018, pp. 65–80.

H. Vaghashia and A. Ganatra, "A survey: privacy preservation techniques in data mining," International Journal of Computer Applications, vol. 119, no. 4, pp. 20–26, 2015. DOI: https://doi.org/10.5120/21056-3704

Y. A. A. S. Aldeen, M. Salleh, and M. A. Razzaque, "A comprehensive review on privacy preserving data mining," SpringerPlus, vol. 4, no. 1, Nov. 2015, Art. no. 694. DOI: https://doi.org/10.1186/s40064-015-1481-x

H. Chabanne, A. de Wargny, J. Milgram, C. Morel, and E. Prouff, "Privacy-Preserving Classification on Deep Neural Network." Cryptology ePrint Archive, 2017.

K. Bonawitz et al., "Practical Secure Aggregation for Privacy-Preserving Machine Learning," in Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security, Dallas, TX, USA, July 2017, pp. 1175–1191. DOI: https://doi.org/10.1145/3133956.3133982

J. J. LaViola, "Double exponential smoothing: an alternative to Kalman filter-based predictive tracking," in Proceedings of the workshop on Virtual environments 2003, Zurich, Switzerland, Feb. 2003, pp. 199–206. DOI: https://doi.org/10.1145/769953.769976

B. E. Sabir, M. Youssfi, O. Bouattane, and H. Allali, "Towards a New Model to Secure IoT-based Smart Home Mobile Agents using Blockchain Technology," Engineering, Technology & Applied Science Research, vol. 10, no. 2, pp. 5441–5447, Apr. 2020. DOI: https://doi.org/10.48084/etasr.3394

H. Taud and J. F. Mas, "Multilayer Perceptron (MLP)," in Geomatic Approaches for Modeling Land Change Scenarios, M. T. Camacho Olmedo, M. Paegelow, J.-F. Mas, and F. Escobar, Eds. Springer International Publishing, 2018, pp. 451–455. DOI: https://doi.org/10.1007/978-3-319-60801-3_27

W. S. A. Janosi, W. Steinburn, M. Pfisterer, and R. Detrano, "Heart Disease." UCI Machine Learning Repository, 1989.